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import numpy as np

np_data = np.arange(6).reshape((2, 3))
torch_d">
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                <h1 id="Pytorch学习笔记"><a href="#Pytorch学习笔记" class="headerlink" title="Pytorch学习笔记"></a>Pytorch学习笔记</h1><h3 id="Pytorch零基础篇"><a href="#Pytorch零基础篇" class="headerlink" title="Pytorch零基础篇"></a>Pytorch零基础篇</h3><h4 id="torch-和numpy之间的数据转换"><a href="#torch-和numpy之间的数据转换" class="headerlink" title="torch 和numpy之间的数据转换"></a>torch 和numpy之间的数据转换</h4><pre class="line-numbers language-python" data-language="python"><code class="language-python"><span class="token keyword">import</span> torch
<span class="token keyword">import</span> numpy <span class="token keyword">as</span> np

np_data <span class="token operator">=</span> np<span class="token punctuation">.</span>arange<span class="token punctuation">(</span><span class="token number">6</span><span class="token punctuation">)</span><span class="token punctuation">.</span>reshape<span class="token punctuation">(</span><span class="token punctuation">(</span><span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">)</span><span class="token punctuation">)</span>
torch_data <span class="token operator">=</span> torch<span class="token punctuation">.</span>from_numpy<span class="token punctuation">(</span>np_data<span class="token punctuation">)</span>

tensor2array <span class="token operator">=</span> torch_data<span class="token punctuation">.</span>numpy<span class="token punctuation">(</span><span class="token punctuation">)</span>

<span class="token keyword">print</span><span class="token punctuation">(</span>tensor2array<span class="token punctuation">)</span>
<span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string">"np_data"</span><span class="token punctuation">,</span>np_data<span class="token punctuation">,</span>
      <span class="token string">"\ntoch_data"</span><span class="token punctuation">,</span>torch_data<span class="token punctuation">)</span><span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>

<h4 id="Tensor运算"><a href="#Tensor运算" class="headerlink" title="Tensor运算"></a>Tensor运算</h4><pre class="line-numbers language-py" data-language="py"><code class="language-py"><span class="token keyword">import</span> torch
<span class="token keyword">import</span> numpy <span class="token keyword">as</span> np

<span class="token comment"># 运算符号</span>
<span class="token comment"># abs</span>
data <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">,</span> <span class="token operator">-</span><span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">]</span>
tensor <span class="token operator">=</span> torch<span class="token punctuation">.</span>FloatTensor<span class="token punctuation">(</span>data<span class="token punctuation">)</span>  <span class="token comment"># 32bit</span>
<span class="token keyword">print</span><span class="token punctuation">(</span>
    <span class="token string">"\nabs"</span><span class="token punctuation">,</span>
    <span class="token string">"\nnumpy:"</span><span class="token punctuation">,</span>np<span class="token punctuation">.</span><span class="token builtin">abs</span><span class="token punctuation">(</span>data<span class="token punctuation">)</span><span class="token punctuation">,</span>
    <span class="token string">"\ntorch:"</span><span class="token punctuation">,</span>torch<span class="token punctuation">.</span><span class="token builtin">abs</span><span class="token punctuation">(</span>tensor<span class="token punctuation">)</span>
<span class="token punctuation">)</span><span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>





<table>
<thead>
<tr>
<th>函数</th>
<th>作用</th>
</tr>
</thead>
<tbody><tr>
<td>abs</td>
<td>绝对值</td>
</tr>
<tr>
<td>sin/cos/tan</td>
<td>三角函数值</td>
</tr>
<tr>
<td>mean</td>
<td>平均值</td>
</tr>
</tbody></table>
<p>更多查看官方文档</p>
<h4 id="Pytorch手写数据集MINIST"><a href="#Pytorch手写数据集MINIST" class="headerlink" title="Pytorch手写数据集MINIST"></a>Pytorch手写数据集MINIST</h4><pre class="line-numbers language-python" data-language="python"><code class="language-python"><span class="token keyword">import</span> torch
<span class="token keyword">import</span> torch<span class="token punctuation">.</span>nn <span class="token keyword">as</span> nn
<span class="token keyword">import</span> torch<span class="token punctuation">.</span>nn<span class="token punctuation">.</span>functional <span class="token keyword">as</span> F
<span class="token keyword">import</span> torch<span class="token punctuation">.</span>optim <span class="token keyword">as</span> optim
<span class="token keyword">from</span> torchvision <span class="token keyword">import</span> datasets<span class="token punctuation">,</span> transforms

<span class="token comment"># 定义超参</span>
BATCH_SIZE <span class="token operator">=</span> <span class="token number">512</span>
EPOCHS <span class="token operator">=</span> <span class="token number">20</span>
DEVICE <span class="token operator">=</span> torch<span class="token punctuation">.</span>device<span class="token punctuation">(</span><span class="token string">"cuda"</span> <span class="token keyword">if</span> torch<span class="token punctuation">.</span>cuda<span class="token punctuation">.</span>is_available<span class="token punctuation">(</span><span class="token punctuation">)</span> <span class="token keyword">else</span> <span class="token string">"cpu"</span><span class="token punctuation">)</span>

<span class="token comment"># 下载训练集</span>
train_loader <span class="token operator">=</span> torch<span class="token punctuation">.</span>utils<span class="token punctuation">.</span>data<span class="token punctuation">.</span>DataLoader<span class="token punctuation">(</span>
    datasets<span class="token punctuation">.</span>MNIST<span class="token punctuation">(</span><span class="token string">'data'</span><span class="token punctuation">,</span> train <span class="token operator">=</span> <span class="token boolean">True</span><span class="token punctuation">,</span> download <span class="token operator">=</span> <span class="token boolean">True</span><span class="token punctuation">,</span>
              transform <span class="token operator">=</span> transforms<span class="token punctuation">.</span>Compose<span class="token punctuation">(</span><span class="token punctuation">[</span>
                  transforms<span class="token punctuation">.</span>ToTensor<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">,</span>
                  transforms<span class="token punctuation">.</span>Normalize<span class="token punctuation">(</span><span class="token punctuation">(</span><span class="token number">0.1037</span><span class="token punctuation">,</span><span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token punctuation">(</span><span class="token number">0.3081</span><span class="token punctuation">,</span><span class="token punctuation">)</span><span class="token punctuation">)</span>
              <span class="token punctuation">]</span><span class="token punctuation">)</span><span class="token punctuation">)</span><span class="token punctuation">,</span>
batch_size <span class="token operator">=</span> BATCH_SIZE<span class="token punctuation">,</span> shuffle <span class="token operator">=</span> <span class="token boolean">True</span><span class="token punctuation">)</span>

<span class="token comment"># 测试集</span>
test_loader <span class="token operator">=</span> torch<span class="token punctuation">.</span>utils<span class="token punctuation">.</span>data<span class="token punctuation">.</span>DataLoader<span class="token punctuation">(</span>
datasets<span class="token punctuation">.</span>MNIST<span class="token punctuation">(</span><span class="token string">'data'</span><span class="token punctuation">,</span> train <span class="token operator">=</span> <span class="token boolean">False</span><span class="token punctuation">,</span> transform <span class="token operator">=</span> transforms<span class="token punctuation">.</span>Compose<span class="token punctuation">(</span><span class="token punctuation">[</span>
    transforms<span class="token punctuation">.</span>ToTensor<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">,</span>
    transforms<span class="token punctuation">.</span>Normalize<span class="token punctuation">(</span><span class="token punctuation">(</span><span class="token number">0.1037</span><span class="token punctuation">,</span><span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token punctuation">(</span><span class="token number">0.3081</span><span class="token punctuation">,</span><span class="token punctuation">)</span><span class="token punctuation">)</span>
<span class="token punctuation">]</span><span class="token punctuation">)</span><span class="token punctuation">)</span><span class="token punctuation">,</span>
batch_size <span class="token operator">=</span> BATCH_SIZE<span class="token punctuation">,</span> shuffle <span class="token operator">=</span> <span class="token boolean">True</span><span class="token punctuation">)</span>


<span class="token comment"># 定义模型</span>
<span class="token keyword">class</span> <span class="token class-name">ConvNet</span><span class="token punctuation">(</span>nn<span class="token punctuation">.</span>Module<span class="token punctuation">)</span><span class="token punctuation">:</span>
    <span class="token keyword">def</span> <span class="token function">__init__</span><span class="token punctuation">(</span>self<span class="token punctuation">)</span><span class="token punctuation">:</span>
        <span class="token builtin">super</span><span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">.</span>__init__<span class="token punctuation">(</span><span class="token punctuation">)</span>
        <span class="token comment"># 1*1*28*28</span>
        self<span class="token punctuation">.</span>conv1 <span class="token operator">=</span> nn<span class="token punctuation">.</span>Conv2d<span class="token punctuation">(</span><span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">10</span><span class="token punctuation">,</span> <span class="token number">5</span><span class="token punctuation">)</span>
        self<span class="token punctuation">.</span>conv2 <span class="token operator">=</span> nn<span class="token punctuation">.</span>Conv2d<span class="token punctuation">(</span><span class="token number">10</span><span class="token punctuation">,</span> <span class="token number">20</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">)</span>
        self<span class="token punctuation">.</span>fc1 <span class="token operator">=</span> nn<span class="token punctuation">.</span>Linear<span class="token punctuation">(</span><span class="token number">20</span> <span class="token operator">*</span> <span class="token number">10</span> <span class="token operator">*</span> <span class="token number">10</span><span class="token punctuation">,</span> <span class="token number">500</span><span class="token punctuation">)</span>
        self<span class="token punctuation">.</span>fc2 <span class="token operator">=</span> nn<span class="token punctuation">.</span>Linear<span class="token punctuation">(</span><span class="token number">500</span><span class="token punctuation">,</span> <span class="token number">10</span><span class="token punctuation">)</span>

    <span class="token keyword">def</span> <span class="token function">forward</span><span class="token punctuation">(</span>self<span class="token punctuation">,</span> x<span class="token punctuation">)</span><span class="token punctuation">:</span>
        in_size <span class="token operator">=</span> x<span class="token punctuation">.</span>size<span class="token punctuation">(</span><span class="token number">0</span><span class="token punctuation">)</span>
        out <span class="token operator">=</span> self<span class="token punctuation">.</span>conv1<span class="token punctuation">(</span>x<span class="token punctuation">)</span>  <span class="token comment"># 1* 10 * 24 *24</span>
        out <span class="token operator">=</span> F<span class="token punctuation">.</span>relu<span class="token punctuation">(</span>out<span class="token punctuation">)</span>
        out <span class="token operator">=</span> F<span class="token punctuation">.</span>max_pool2d<span class="token punctuation">(</span>out<span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">)</span>  <span class="token comment"># 1* 10 * 12 * 12</span>
        out <span class="token operator">=</span> self<span class="token punctuation">.</span>conv2<span class="token punctuation">(</span>out<span class="token punctuation">)</span>  <span class="token comment"># 1* 20 * 10 * 10</span>
        out <span class="token operator">=</span> F<span class="token punctuation">.</span>relu<span class="token punctuation">(</span>out<span class="token punctuation">)</span>
        out <span class="token operator">=</span> out<span class="token punctuation">.</span>view<span class="token punctuation">(</span>in_size<span class="token punctuation">,</span> <span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">)</span>  <span class="token comment"># 1 * 2000</span>
        out <span class="token operator">=</span> self<span class="token punctuation">.</span>fc1<span class="token punctuation">(</span>out<span class="token punctuation">)</span>  <span class="token comment"># 1 * 500</span>
        out <span class="token operator">=</span> F<span class="token punctuation">.</span>relu<span class="token punctuation">(</span>out<span class="token punctuation">)</span>
        out <span class="token operator">=</span> self<span class="token punctuation">.</span>fc2<span class="token punctuation">(</span>out<span class="token punctuation">)</span>  <span class="token comment"># 1 * 10</span>
        out <span class="token operator">=</span> F<span class="token punctuation">.</span>log_softmax<span class="token punctuation">(</span>out<span class="token punctuation">,</span> dim<span class="token operator">=</span><span class="token number">1</span><span class="token punctuation">)</span>
        <span class="token keyword">return</span> out

<span class="token comment">#生成模型和优化器</span>
model <span class="token operator">=</span> ConvNet<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">.</span>to<span class="token punctuation">(</span>DEVICE<span class="token punctuation">)</span>
optimizer <span class="token operator">=</span> optim<span class="token punctuation">.</span>Adam<span class="token punctuation">(</span>model<span class="token punctuation">.</span>parameters<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">)</span>


<span class="token comment"># 定义训练函数</span>
<span class="token keyword">def</span> <span class="token function">train</span><span class="token punctuation">(</span>model<span class="token punctuation">,</span> device<span class="token punctuation">,</span> train_loader<span class="token punctuation">,</span> optimizer<span class="token punctuation">,</span> epoch<span class="token punctuation">)</span><span class="token punctuation">:</span>
    model<span class="token punctuation">.</span>train<span class="token punctuation">(</span><span class="token punctuation">)</span>
    <span class="token keyword">for</span> batch_idx<span class="token punctuation">,</span> <span class="token punctuation">(</span>data<span class="token punctuation">,</span> target<span class="token punctuation">)</span> <span class="token keyword">in</span> <span class="token builtin">enumerate</span><span class="token punctuation">(</span>train_loader<span class="token punctuation">)</span><span class="token punctuation">:</span>
        data<span class="token punctuation">,</span> target <span class="token operator">=</span> data<span class="token punctuation">.</span>to<span class="token punctuation">(</span>device<span class="token punctuation">)</span><span class="token punctuation">,</span> target<span class="token punctuation">.</span>to<span class="token punctuation">(</span>device<span class="token punctuation">)</span>
        optimizer<span class="token punctuation">.</span>zero_grad<span class="token punctuation">(</span><span class="token punctuation">)</span>
        output <span class="token operator">=</span> model<span class="token punctuation">(</span>data<span class="token punctuation">)</span>
        loss <span class="token operator">=</span> F<span class="token punctuation">.</span>nll_loss<span class="token punctuation">(</span>output<span class="token punctuation">,</span> target<span class="token punctuation">)</span>
        loss<span class="token punctuation">.</span>backward<span class="token punctuation">(</span><span class="token punctuation">)</span>
        optimizer<span class="token punctuation">.</span>step<span class="token punctuation">(</span><span class="token punctuation">)</span>
        <span class="token keyword">if</span> <span class="token punctuation">(</span>batch_idx <span class="token operator">+</span> <span class="token number">1</span><span class="token punctuation">)</span> <span class="token operator">%</span> <span class="token number">30</span> <span class="token operator">==</span> <span class="token number">0</span><span class="token punctuation">:</span>
            <span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string">'Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'</span><span class="token punctuation">.</span><span class="token builtin">format</span><span class="token punctuation">(</span>
                epoch<span class="token punctuation">,</span> batch_idx <span class="token operator">*</span> <span class="token builtin">len</span><span class="token punctuation">(</span>data<span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token builtin">len</span><span class="token punctuation">(</span>train_loader<span class="token punctuation">.</span>dataset<span class="token punctuation">)</span><span class="token punctuation">,</span>
                       <span class="token number">100</span><span class="token punctuation">.</span> <span class="token operator">*</span> batch_idx <span class="token operator">/</span> <span class="token builtin">len</span><span class="token punctuation">(</span>train_loader<span class="token punctuation">)</span><span class="token punctuation">,</span> loss<span class="token punctuation">.</span>item<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">)</span><span class="token punctuation">)</span>


<span class="token comment"># 定义测试函数</span>
<span class="token keyword">def</span> <span class="token function">test</span><span class="token punctuation">(</span>model<span class="token punctuation">,</span> device<span class="token punctuation">,</span> test_loader<span class="token punctuation">)</span><span class="token punctuation">:</span>
    model<span class="token punctuation">.</span><span class="token builtin">eval</span><span class="token punctuation">(</span><span class="token punctuation">)</span>
    test_loss <span class="token operator">=</span> <span class="token number">0</span>
    correct <span class="token operator">=</span> <span class="token number">0</span>
    <span class="token keyword">with</span> torch<span class="token punctuation">.</span>no_grad<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">:</span>
        <span class="token keyword">for</span> data<span class="token punctuation">,</span> target <span class="token keyword">in</span> test_loader<span class="token punctuation">:</span>
            data<span class="token punctuation">,</span> target <span class="token operator">=</span> data<span class="token punctuation">.</span>to<span class="token punctuation">(</span>device<span class="token punctuation">)</span><span class="token punctuation">,</span> target<span class="token punctuation">.</span>to<span class="token punctuation">(</span>device<span class="token punctuation">)</span>
            output <span class="token operator">=</span> model<span class="token punctuation">(</span>data<span class="token punctuation">)</span>
            test_loss <span class="token operator">+=</span> F<span class="token punctuation">.</span>nll_loss<span class="token punctuation">(</span>output<span class="token punctuation">,</span> target<span class="token punctuation">,</span> reduction<span class="token operator">=</span><span class="token string">'sum'</span><span class="token punctuation">)</span>  <span class="token comment"># 将一批的损失相加</span>
            pred <span class="token operator">=</span> output<span class="token punctuation">.</span><span class="token builtin">max</span><span class="token punctuation">(</span><span class="token number">1</span><span class="token punctuation">,</span> keepdim<span class="token operator">=</span><span class="token boolean">True</span><span class="token punctuation">)</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">]</span>  <span class="token comment"># 找到概率最大的下标</span>
            correct <span class="token operator">+=</span> pred<span class="token punctuation">.</span>eq<span class="token punctuation">(</span>target<span class="token punctuation">.</span>view_as<span class="token punctuation">(</span>pred<span class="token punctuation">)</span><span class="token punctuation">)</span><span class="token punctuation">.</span><span class="token builtin">sum</span><span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">.</span>item<span class="token punctuation">(</span><span class="token punctuation">)</span>

    test_loss <span class="token operator">/=</span> <span class="token builtin">len</span><span class="token punctuation">(</span>test_loader<span class="token punctuation">.</span>dataset<span class="token punctuation">)</span>
    <span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string">"\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%) \n"</span><span class="token punctuation">.</span><span class="token builtin">format</span><span class="token punctuation">(</span>
        test_loss<span class="token punctuation">,</span> correct<span class="token punctuation">,</span> <span class="token builtin">len</span><span class="token punctuation">(</span>test_loader<span class="token punctuation">.</span>dataset<span class="token punctuation">)</span><span class="token punctuation">,</span>
        <span class="token number">100</span><span class="token punctuation">.</span> <span class="token operator">*</span> correct <span class="token operator">/</span> <span class="token builtin">len</span><span class="token punctuation">(</span>test_loader<span class="token punctuation">.</span>dataset<span class="token punctuation">)</span>
    <span class="token punctuation">)</span><span class="token punctuation">)</span>


<span class="token keyword">for</span> epoch <span class="token keyword">in</span> <span class="token builtin">range</span><span class="token punctuation">(</span><span class="token number">1</span><span class="token punctuation">,</span> EPOCHS <span class="token operator">+</span> <span class="token number">1</span><span class="token punctuation">)</span><span class="token punctuation">:</span>
    train<span class="token punctuation">(</span>model<span class="token punctuation">,</span>  DEVICE<span class="token punctuation">,</span> train_loader<span class="token punctuation">,</span> optimizer<span class="token punctuation">,</span> epoch<span class="token punctuation">)</span>
    test<span class="token punctuation">(</span>model<span class="token punctuation">,</span> DEVICE<span class="token punctuation">,</span> test_loader<span class="token punctuation">)</span>
<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>





<h4 id="Pytorch使用cuda与使用CPU比较"><a href="#Pytorch使用cuda与使用CPU比较" class="headerlink" title="Pytorch使用cuda与使用CPU比较"></a>Pytorch使用cuda与使用CPU比较</h4><p>检测GPU是否可用：</p>
<pre class="line-numbers language-python" data-language="python"><code class="language-python"><span class="token keyword">import</span> torch
flag <span class="token operator">=</span> torch<span class="token punctuation">.</span>cuda<span class="token punctuation">.</span>is_available<span class="token punctuation">(</span><span class="token punctuation">)</span>
<span class="token keyword">if</span> flag<span class="token punctuation">:</span>
    <span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string">"CUDA可使用"</span><span class="token punctuation">)</span>
<span class="token keyword">else</span><span class="token punctuation">:</span>
    <span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string">"CUDA不可用"</span><span class="token punctuation">)</span>

ngpu<span class="token operator">=</span> <span class="token number">1</span>
<span class="token comment"># Decide which device we want to run on</span>
device <span class="token operator">=</span> torch<span class="token punctuation">.</span>device<span class="token punctuation">(</span><span class="token string">"cuda:0"</span> <span class="token keyword">if</span> <span class="token punctuation">(</span>torch<span class="token punctuation">.</span>cuda<span class="token punctuation">.</span>is_available<span class="token punctuation">(</span><span class="token punctuation">)</span> <span class="token keyword">and</span> ngpu <span class="token operator">&gt;</span> <span class="token number">0</span><span class="token punctuation">)</span> <span class="token keyword">else</span> <span class="token string">"cpu"</span><span class="token punctuation">)</span>
<span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string">"驱动为："</span><span class="token punctuation">,</span>device<span class="token punctuation">)</span>
<span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string">"GPU型号： "</span><span class="token punctuation">,</span>torch<span class="token punctuation">.</span>cuda<span class="token punctuation">.</span>get_device_name<span class="token punctuation">(</span><span class="token number">0</span><span class="token punctuation">)</span><span class="token punctuation">)</span><span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>



<pre class="line-numbers language-python" data-language="python"><code class="language-python"><span class="token keyword">import</span> torch
<span class="token keyword">import</span> time

<span class="token keyword">print</span><span class="token punctuation">(</span>torch<span class="token punctuation">.</span>__version__<span class="token punctuation">)</span>
<span class="token keyword">print</span><span class="token punctuation">(</span>torch<span class="token punctuation">.</span>cuda<span class="token punctuation">.</span>is_available<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">)</span>

a <span class="token operator">=</span> torch<span class="token punctuation">.</span>randn<span class="token punctuation">(</span><span class="token number">20000</span><span class="token punctuation">,</span> <span class="token number">9000</span><span class="token punctuation">)</span>
b <span class="token operator">=</span> torch<span class="token punctuation">.</span>randn<span class="token punctuation">(</span><span class="token number">9000</span><span class="token punctuation">,</span> <span class="token number">2000</span><span class="token punctuation">)</span>

t0 <span class="token operator">=</span> time<span class="token punctuation">.</span>time<span class="token punctuation">(</span><span class="token punctuation">)</span>
c <span class="token operator">=</span> torch<span class="token punctuation">.</span>matmul<span class="token punctuation">(</span>a<span class="token punctuation">,</span> b<span class="token punctuation">)</span> <span class="token comment"># 矩阵乘法</span>
t1 <span class="token operator">=</span> time<span class="token punctuation">.</span>time<span class="token punctuation">(</span><span class="token punctuation">)</span>
<span class="token keyword">print</span><span class="token punctuation">(</span>a<span class="token punctuation">.</span>device<span class="token punctuation">,</span> t1 <span class="token operator">-</span> t0<span class="token punctuation">,</span> c<span class="token punctuation">.</span>norm<span class="token punctuation">(</span><span class="token number">2</span><span class="token punctuation">)</span><span class="token punctuation">)</span>

t0 <span class="token operator">=</span> time<span class="token punctuation">.</span>time<span class="token punctuation">(</span><span class="token punctuation">)</span>
c <span class="token operator">=</span> torch<span class="token punctuation">.</span>matmul<span class="token punctuation">(</span>a<span class="token punctuation">,</span> b<span class="token punctuation">)</span> <span class="token comment"># 矩阵乘法</span>
t1 <span class="token operator">=</span> time<span class="token punctuation">.</span>time<span class="token punctuation">(</span><span class="token punctuation">)</span>
<span class="token keyword">print</span><span class="token punctuation">(</span>a<span class="token punctuation">.</span>device<span class="token punctuation">,</span> t1 <span class="token operator">-</span> t0<span class="token punctuation">,</span> c<span class="token punctuation">.</span>norm<span class="token punctuation">(</span><span class="token number">2</span><span class="token punctuation">)</span><span class="token punctuation">)</span>

<span class="token comment">#使用GPU来计算</span>
device <span class="token operator">=</span> torch<span class="token punctuation">.</span>device<span class="token punctuation">(</span><span class="token string">'cuda'</span><span class="token punctuation">)</span>
a <span class="token operator">=</span> a<span class="token punctuation">.</span>to<span class="token punctuation">(</span>device<span class="token punctuation">)</span>
b <span class="token operator">=</span> b<span class="token punctuation">.</span>to<span class="token punctuation">(</span>device<span class="token punctuation">)</span>

t0 <span class="token operator">=</span> time<span class="token punctuation">.</span>time<span class="token punctuation">(</span><span class="token punctuation">)</span>
c <span class="token operator">=</span> torch<span class="token punctuation">.</span>matmul<span class="token punctuation">(</span>a<span class="token punctuation">,</span> b<span class="token punctuation">)</span>
t1 <span class="token operator">=</span> time<span class="token punctuation">.</span>time<span class="token punctuation">(</span><span class="token punctuation">)</span>
<span class="token keyword">print</span><span class="token punctuation">(</span>a<span class="token punctuation">.</span>device<span class="token punctuation">,</span> t1 <span class="token operator">-</span> t0<span class="token punctuation">,</span> c<span class="token punctuation">.</span>norm<span class="token punctuation">(</span><span class="token number">2</span><span class="token punctuation">)</span><span class="token punctuation">)</span>


to <span class="token operator">=</span> time<span class="token punctuation">.</span>time<span class="token punctuation">(</span><span class="token punctuation">)</span>
c <span class="token operator">=</span> torch<span class="token punctuation">.</span>matmul<span class="token punctuation">(</span>a<span class="token punctuation">,</span>b<span class="token punctuation">)</span>
t1 <span class="token operator">=</span> time<span class="token punctuation">.</span>time<span class="token punctuation">(</span><span class="token punctuation">)</span>
<span class="token keyword">print</span><span class="token punctuation">(</span>a<span class="token punctuation">.</span>device<span class="token punctuation">,</span> t1 <span class="token operator">-</span> t0<span class="token punctuation">,</span> c<span class="token punctuation">.</span>norm<span class="token punctuation">(</span><span class="token number">2</span><span class="token punctuation">)</span><span class="token punctuation">)</span>

<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>

<p>输出结果：</p>
<pre class="line-numbers language-none"><code class="language-none">1.3.1+cu92
True
cpu 5.827749252319336 tensor(586883.4375)
cpu 6.121405601501465 tensor(586883.4375)
cuda:0 0.2789483070373535 tensor(599939.1250, device='cuda:0')
cuda:0 1.1803054809570312 tensor(599939.1250, device='cuda:0')

Process finished with exit code 0<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>

<p>使用GPU加速后可以显著提高速度。</p>
<h4 id="pytorch自动求导"><a href="#pytorch自动求导" class="headerlink" title="pytorch自动求导"></a>pytorch自动求导</h4><pre class="line-numbers language-python" data-language="python"><code class="language-python"><span class="token keyword">import</span> torch
<span class="token keyword">from</span> torch <span class="token keyword">import</span> autograd

x <span class="token operator">=</span> torch<span class="token punctuation">.</span>tensor<span class="token punctuation">(</span><span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">)</span>
a <span class="token operator">=</span> torch<span class="token punctuation">.</span>tensor<span class="token punctuation">(</span><span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> requires_grad<span class="token operator">=</span><span class="token boolean">True</span><span class="token punctuation">)</span>  <span class="token comment"># requires_grad表明是对这个变量求导</span>
b <span class="token operator">=</span> torch<span class="token punctuation">.</span>tensor<span class="token punctuation">(</span><span class="token number">2</span><span class="token punctuation">.</span><span class="token punctuation">,</span> requires_grad<span class="token operator">=</span><span class="token boolean">True</span><span class="token punctuation">)</span>
c <span class="token operator">=</span> torch<span class="token punctuation">.</span>tensor<span class="token punctuation">(</span><span class="token number">3</span><span class="token punctuation">.</span><span class="token punctuation">,</span> requires_grad<span class="token operator">=</span><span class="token boolean">True</span><span class="token punctuation">)</span>

y <span class="token operator">=</span> a<span class="token operator">**</span><span class="token number">2</span> <span class="token operator">*</span> x <span class="token operator">+</span> b<span class="token operator">*</span>x <span class="token operator">+</span> c

<span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string">"before: "</span> <span class="token punctuation">,</span> a<span class="token punctuation">.</span>grad<span class="token punctuation">,</span> b<span class="token punctuation">.</span>grad<span class="token punctuation">,</span> c<span class="token punctuation">.</span>grad<span class="token punctuation">)</span>
grads <span class="token operator">=</span> autograd<span class="token punctuation">.</span>grad<span class="token punctuation">(</span>y<span class="token punctuation">,</span> <span class="token punctuation">[</span>a<span class="token punctuation">,</span>b<span class="token punctuation">,</span>c<span class="token punctuation">]</span><span class="token punctuation">)</span>
<span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string">"after"</span><span class="token punctuation">,</span> grads<span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">,</span> grads<span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">,</span> grads<span class="token punctuation">[</span><span class="token number">2</span><span class="token punctuation">]</span><span class="token punctuation">)</span><span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>

<p>输出：</p>
<pre class="line-numbers language-python" data-language="python"><code class="language-python">before<span class="token punctuation">:</span>  <span class="token boolean">None</span> <span class="token boolean">None</span> <span class="token boolean">None</span>
after tensor<span class="token punctuation">(</span><span class="token number">2</span><span class="token punctuation">.</span><span class="token punctuation">)</span> tensor<span class="token punctuation">(</span><span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">)</span> tensor<span class="token punctuation">(</span><span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">)</span><span aria-hidden="true" class="line-numbers-rows"><span></span><span></span></span></code></pre>





<h3 id="Pytorch源码解析"><a href="#Pytorch源码解析" class="headerlink" title="Pytorch源码解析"></a>Pytorch源码解析</h3><h4 id="1-ToTensor"><a href="#1-ToTensor" class="headerlink" title="1. ToTensor()"></a>1. ToTensor()</h4><p><code>torchvision.transforms.ToTensor()</code></p>
<p>class ToTensor:</p>
<p>  Convert a <code>PIL Image</code> or <code>numpy.ndarray</code> to tensor. This transform does not support torchscript.</p>
<p>  Converts a PIL Image or numpy.ndarray (H x W x C) in the range</p>
<p>  [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0]</p>
<p>  if the PIL Image belongs to one of the modes (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1)</p>
<p>  or if the numpy.ndarray has dtype = np.uint8</p>

                
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                $input.addEventListener('input', function () {
                    var str = '<ul class=\"search-result-list\">';
                    var keywords = this.value.trim().toLowerCase().split(/[\s\-]+/);
                    $resultContent.innerHTML = "";
                    if (this.value.trim().length <= 0) {
                        return;
                    }
                    // perform local searching
                    datas.forEach(function (data) {
                        var isMatch = true;
                        var data_title = data.title.trim().toLowerCase();
                        var data_content = data.content.trim().replace(/<[^>]+>/g, "").toLowerCase();
                        var data_url = data.url;
                        data_url = data_url.indexOf('/') === 0 ? data.url : '/' + data_url;
                        var index_title = -1;
                        var index_content = -1;
                        var first_occur = -1;
                        // only match artiles with not empty titles and contents
                        if (data_title !== '' && data_content !== '') {
                            keywords.forEach(function (keyword, i) {
                                index_title = data_title.indexOf(keyword);
                                index_content = data_content.indexOf(keyword);
                                if (index_title < 0 && index_content < 0) {
                                    isMatch = false;
                                } else {
                                    if (index_content < 0) {
                                        index_content = 0;
                                    }
                                    if (i === 0) {
                                        first_occur = index_content;
                                    }
                                }
                            });
                        }
                        // show search results
                        if (isMatch) {
                            str += "<li><a href='" + data_url + "' class='search-result-title'>" + data_title + "</a>";
                            var content = data.content.trim().replace(/<[^>]+>/g, "");
                            if (first_occur >= 0) {
                                // cut out 100 characters
                                var start = first_occur - 20;
                                var end = first_occur + 80;
                                if (start < 0) {
                                    start = 0;
                                }
                                if (start === 0) {
                                    end = 100;
                                }
                                if (end > content.length) {
                                    end = content.length;
                                }
                                var match_content = content.substr(start, end);
                                // highlight all keywords
                                keywords.forEach(function (keyword) {
                                    var regS = new RegExp(keyword, "gi");
                                    match_content = match_content.replace(regS, "<em class=\"search-keyword\">" + keyword + "</em>");
                                });

                                str += "<p class=\"search-result\">" + match_content + "...</p>"
                            }
                            str += "</li>";
                        }
                    });
                    str += "</ul>";
                    $resultContent.innerHTML = str;
                });
            }
        });
    };

    searchFunc('/search.xml', 'searchInput', 'searchResult');
});
</script>

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